Context Information for Understanding Forest Fire Using Evolutionary Computation

  • L. Usero
  • A. Arroyo
  • J. Calvo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)


One of the major forces for understanding forest fire risk and behavior is the fire fuel. Fire risk and behavior depend on the fuel properties such as moisture content. Context information on vegetation water content is vital for understanding the processes involved in initiation and propagation of forest fires. In that sense, a novel method was tested to estimate vegetation canopy water content (CWC) from simulated MODIS satellite data. An inversion of a radiative transfer model called Forest Light Interaction-Model (FLIM) from performed using evolutionary computation. CWC is critical, among other applications, in wildfire risk assessment since a decrease in CWC causes higher probability to have wildfire occurrence. Simulations were carried out with the FLIM model for a wide range of forest canopy characteristics and CWC values. A 50 subsample of the simulations was used for the training process and 50 for the validation providing a RMSE=0.74 and r2=0.62. Further research is needed to apply this method on real MODIS images.


Genetic Programing Vegetation Water Content Forest Fire Understanding 


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • L. Usero
    • 3
    • 4
  • A. Arroyo
    • 2
  • J. Calvo
    • 1
  1. 1.Dpto. de Organización y Estructura de la información, Universidad Politécnica de MadridSpain
  2. 2.Dpto. de Sistemas Inteligentes Aplicados, Universidad Politécnica de MadridSpain
  3. 3.Dpto. Ciencias de la Computación, Universidad de AlcaláSpain
  4. 4.Center for Spatial Technologies and Remote Sensing, U. California. One Shields Ave. 95616-8617 Davis, CA.USA

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